Senior Staff MLOps Engineer

As a senior leader in MLOps, you will own the architecture, standards, and technical direction for the AI Accelerator, enabling models to move from experimentation to reliable production. You will define tooling, platforms, and practices to support large-scale model training, deployment, monitoring, and governance. This strategic yet hands-on role sits within Computational Innovation, partnering with cross-functional teams to deliver production-quality AI that explains disease biology and drives therapeutic discovery. You will shape the ML platform and mentor the team, contributing to a high-impact strategic initiative for BI.

• Own the MLOps architecture and roadmap for the AI Accelerator, covering training orchestration, experiment tracking, model registries, CI/CD, deployment and monitoring
• Establish MLOps standards and engineering practices (testing, containerisation, release management, production operations)
• Define artefact management standards (weights, hyperparameters, configuration, model cards) for governance and traceability
• Enable large-scale distributed training and federated learning with AI Infrastructure to accelerate research-to-production
• Drive cost efficiency across training and inference workloads on enterprise infrastructure
• Coach and mentor MLOps engineers, establish engineering principles, and act as senior escalation point for complex challenges

• PhD or MSc and equivalent experience in a STEM subject
• Senior staff level experience in MLOps/ML Platform Engineering with proven strategy/architecture track record
• Deep expertise across the MLOps lifecycle (training orchestration, experiment tracking, model registries, CI/CD, deployment, monitoring)
• Strong software engineering skills with Python; experience building scalable production-grade platforms
• In-depth knowledge of containerisation and orchestration (Docker, Kubernetes, Helm)
• Strong collaboration and influencing skills across research, engineering and business teams

• collaboration
• influencing
• mentoring
• MLOps lifecycle
• training orchestration
• experiment tracking